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Is Sarcasm Detection a Step-by-Step Reasoning Process in Large Language Models?
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Proposes SarcasmCue, a prompting framework with four methods (chain, graph, bagging, tensor of cues) to test whether LLM sarcasm detection is step-by-step, evaluated on four sarcasm benchmarks.
Problem
Whether sarcasm detection, unlike math reasoning, follows a step-by-step reasoning process, and how to prompt LLMs for sarcasm detection beyond sequential chain-of-thought style prompting.
Approach
Four prompting methods spanning sequential (chain of contradiction, graph of cues) and non-sequential (bagging of cues, tensor of cues) structures; ToC fuses linguistic, contextual and emotional cue embeddings via token-wise tensor product and trains only linear projection layers on top of a frozen LLM.
Key results
- CoC reaches 74.74 average macro-F1 on GPT-4o/Claude-era comparisons with Claude 3.5 Sonnet, a 2.0% gain over the best baseline IO (73.26).
- ToC with Llama 3-8B achieves 65.24 average macro-F1, a 29.7% improvement over the best baseline ToT (50.31).
- ToC with Qwen 2-7B reaches 68.39 average macro-F1, versus 43.22 for IO, the best baseline on that model.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use CoC instead of the strongest prompting baseline | GPT-4o · Sarcasm detection, average across four datasets | Macro-F1: 67.88 -> 70.73 | Helped |
| Use CoC instead of the strongest prompting baseline | Claude 3.5 Sonnet · Sarcasm detection, average across four datasets | Macro-F1: 73.26 -> 74.74 | Helped |
| Use ToC instead of the strongest prompting baseline | Llama 3-8B · Sarcasm detection, average across four datasets | Macro-F1: 50.31 -> 65.24 | Helped |
| Use ToC instead of the strongest prompting baseline | Qwen 2-7B · Sarcasm detection, average across four datasets | Macro-F1: 43.22 -> 68.39 | Helped |
| Add linguistic cues to GoC (ablation) | GoC on Claude 3.5 Sonnet · Sarcasm detection, average across four datasets | Macro-F1: 72.78 -> 72.97 | Mixed |
| Add emotional cues to GoC (ablation) | GoC on Claude 3.5 Sonnet · Sarcasm detection, average across four datasets | Macro-F1: 73.24 -> 72.97 | No clear effect |
| Add contextual cues to GoC (ablation) | GoC on Claude 3.5 Sonnet · Sarcasm detection, average across four datasets | Macro-F1: 72.99 -> 72.97 | No clear effect |
| Add linguistic cues to BoC (ablation) | BoC on Claude 3.5 Sonnet · Sarcasm detection, average across four datasets | Macro-F1: 50.30 -> 70.31 | Helped |
| Add emotional cues to BoC (ablation) | BoC on Claude 3.5 Sonnet · Sarcasm detection, average across four datasets | Macro-F1: 60.16 -> 70.31 | Helped |
| Add contextual cues to BoC (ablation) | BoC on Claude 3.5 Sonnet · Sarcasm detection, average across four datasets | Macro-F1: 67.44 -> 70.31 | Helped |
| Add linguistic cues to GoC (ablation) | GoC on Llama 3-8B · Sarcasm detection, average across four datasets | Macro-F1: 50.14 -> 54.54 | Helped |
| Add emotional cues to GoC (ablation) | GoC on Llama 3-8B · Sarcasm detection, average across four datasets | Macro-F1: 48.88 -> 54.54 | Helped |
Surprising findings
- Chain-based CoT underperforms plain IO prompting substantially on GPT-4o (55.67 vs 67.75 average F1).
- CoC performs on par with CoT and IO on Llama 3-8B and Qwen 2-7B rather than clearly beating them.
Limitations
- Only three cue types (linguistic, contextual, emotional) are used; other potentially useful cues are not integrated.
- ToC requires white-box access and supervised training, so it cannot be applied to GPT-4o or Claude 3.5 Sonnet.
- CoC, GoC and BoC show higher false positive rates, indicating over-detection of sarcasm.
Experimental setup
Datasets: IAC-V1 IAC-V2 SemEval 2018 Task 3 MUStARD CMMA UR-FUNNY-V2
Benchmarks: IAC-V1 IAC-V2 SemEval 2018 Task 3 MUStARD CMMA UR-FUNNY-V2
Metrics: accuracy macro-F1
Baselines: IO prompting CoT ToT MFN SVM+BERT
Builds on: chain-of-thought prompting tree of thought graph of thought tensor fusion network
Closest papers by meaning
- Well, Now We Know! Unveiling Sarcasm: Initiating and Exploring Multimodal Conversations with Reasoning (AAAI 2024 · similarity 0.91)
- SEVADE: Self-Evolving Multi-Agent Analysis with Decoupled Evaluation for Hallucination-Resistant Sarcasm Detection (AAAI 2026 · similarity 0.85)
- DocMSU: A Comprehensive Benchmark for Document-Level Multimodal Sarcasm Understanding (AAAI 2024 · similarity 0.85)
- Debiasing Multimodal Sarcasm Detection with Contrastive Learning (AAAI 2024 · similarity 0.84)
- Can Language Models Perform Robust Reasoning in Chain-of-thought Prompting with Noisy Rationales? (NeurIPS 2024 · similarity 0.84)
- Linguini: A benchmark for language-agnostic linguistic reasoning (NeurIPS 2025 · similarity 0.84)
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